Compressed Sensing Verses Auto-Encoder: On the Perspective of Signal Compression and Restoration

Abstract

This paper presents a comparison between compressed sensing (CS) and auto-encoder (AE) for compression and restoration of signals. The study used K-sparse vectors and generated an under-determined system, which is a system of linear equations with fewer equations than unknowns. By using CS and AE under various specific conditions, the accuracy of the signal restoration is compared with mean squared error (MSE). The experimental methodology includes comparing and analyzing the signal recovery performance by altering the algorithm and various parameters. The result represents the performance and accuracy of signal compression and restoration obtained using both techniques. It also provides a comprehensive analysis of CS and AE methods. The importance of this research and the possibility of practical application in various fields are discussed. Overall, this study provides insights into the comparison of CS and AE techniques in the field of sparse signal compression and restoration.